Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks

• Published in: Poultry science 2025-01, Vol.104 (1), p.104648, Article 104648
• Main Authors: Jainonthee, Chalita, Sanwisate, Phutsadee, Sivapirunthep, Panneepa, Chaosap, Chanporn, Mektrirat, Raktham, Chadsuthi, Sudarat, Punyapornwithaya, Veerasak
• Format: Article
• Language: English
• Subjects: Dead on arrival; Machine learning; Meat-type duck; Pre-slaughter mortality; Welfare
• ISSN: 0032-5791; 1525-3171; 1525-3171
• DOI: 10.1016/j.psj.2024.104648

Dead on arrival (DOA) refers to animals, particularly poultry, that die during the pre-slaughter phase. Elevated rates of DOA frequently signify substandard welfare conditions and might stem from multiple causes, resulting in diminished productivity and economic losses. This study included 18,643 truckload entries from 45 farms, encompassing a total of 23,191,809 meat-type ducks sent to a single slaughterhouse in Eastern Thailand between January 2019 and December 2023. The objective of this study was twofold: first, to classify high DOA rates (≥ 0.15%) using several predictors, including season, period of the day, number of ducks per truckload, distance, duration of transportation, age, average body weight, lairage time, and temperature at the lairage area. This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). Additionally, several data-sampling techniques, including oversampling, undersampling, Random Over-Sampling Examples (ROSE), and Synthetic Minority Over-sampling Technique (SMOTE), were utilized to address the issue of imbalanced data. Second, to analyze variable importance contributing to the predictive outcomes. The descriptive analysis revealed a mean DOA percentage of 0.14% (range: 0 to 22.46%, SD = 0.49). The results of the high DOA classification indicated that among all models, XGBoost-Up, XGBoost-Down, and RF-Down were the top three models, achieving the highest overall scores in evaluation metrics including Area Under the ROC Curve (AUC), sensitivity, precision, and F1-score. The primary factors contributing to the high predictive performance of the models were the number of ducks per truckload, temperature at the lairage area, and average body weight. Additionally, the duration and distance of transportation, as well as the period of transportation, were secondary factors contributing to the outcome. These factors should be further investigated to minimize losses during the pre-slaughter phase in meat-type ducks. Additionally, considering these factors when managing transportation can help create conditions that reduce duck deaths.